papaya
Class Linear.Significance
java.lang.Object
papaya.Linear.Significance
- Enclosing class:
- Linear
public static class Linear.Significance
- extends Object
Contains methods used to compute the significance, or pvalue of the input correlations.
The significance is computed using the normal of student-t approximations and hence are
not to be used for small datasets(i.e. size<20).
Method Summary |
static float |
intercept(float intercept,
float interceptStdErr,
int df)
Returns the p-value, or significance, of the computed intercept under the null-hypothesis
of intercept = 0 (two-tailed test). |
static float |
slope(float slope,
float slopeStdErr,
int df)
Returns the p-value, or significance, of the computed slope under the null-hypothesis
of slope = 0 (two-tailed test). |
Linear.Significance
public Linear.Significance()
slope
public static float slope(float slope,
float slopeStdErr,
int df)
- Returns the p-value, or significance, of the computed slope under the null-hypothesis
of slope = 0 (two-tailed test). The p-value is computed using the student-T distribution
with df degrees of freedom and test statistic
t = slope/slopeStdErr,
- Parameters:
slope
- the slope of the best fit linear lineslopeStdErr
- the standard error of the slopedf
- the degrees of freedom (typically (n-2), but could also be (n-1) if the
intercept was previously specified.
intercept
public static float intercept(float intercept,
float interceptStdErr,
int df)
- Returns the p-value, or significance, of the computed intercept under the null-hypothesis
of intercept = 0 (two-tailed test). The p-value is computed using the student-T distribution
with df degrees of freedom and test statistic
t=intercept/interceptStdErr
- Parameters:
intercept
- the intercept of the best fit linear lineinterceptStdErr
- the standard error of the interceptdf
- the degrees of freedom (n-2)
Processing library papaya by
Adila Faruk. (C) 2014